Category Archives : Artificial Intelligence



Rubikloud leverages Azure SQL Data Warehouse to disrupt retail market with accessible AI

In the modern retail environment, consumers are well-informed and expect intuitive, engaging, and informative experiences when they shop. To keep up, retailers need solutions that can help them delight their customers with personalized experiences, empower their workforce to provide differentiated customer experiences, optimize their supply chain with intelligent operations and transform their products and services.

With global scale and intelligence built in to key services, Azure is the perfect platform to build powerful apps to delight retail customers, the possibilities are endless. With a single photo, retailers can create new access points for the customer on a device of their choice. Take a look at this example of what’s possible using Microsoft’s big data and advanced analytics products

AI can be complex, this is where Rubikloud comes in. Rubikloud is focused on accessible AI products for retailers and delivering on the promise of “intelligent decision automation”. They offer a set of SaaS products, Promotion Manager and Customer Lifecycle Manager, that help retailers automate and optimize mass promotional planning and loyalty marketing. These products help retailers reduce the complexities of promotion planning and store allocations and better predict their customers intention and behavior throughout their retail life cycle.

As Rubikloud



From Microsoft Azure to everyone attending NAB Show 2018 — Welcome!

This blog post was authored by Tad Brockway, General Manager, Azure Storage and Azure Stack.

NAB Show is one of my favorites. The creativity, technology, content and storytelling are epic, as is the digital transformation well underway.

This transformation — driven by new businesses models, shifting consumption patterns and technology advancements — will change this great industry. What won’t change is the focus on creators and connecting their content with consumers around the world so they, too, can be a part of the story.

Microsoft’s mission is to “empower every person and every organization on the planet to achieve more.” We are committed to helping everyone in the industry — customers like Endemol Shine, UFA, Jellyfish and Reuters — do just that. With innovations across cloud storage, compute, CDN, Media Services and new investments in Avere Systems and Cycle Cloud, Microsoft Azure is ready to help modernize your media workflows and your business.

How? Well, queue scene…

Your productions are increasingly global and demanding. Azure can help.

Creators, media artists and innovators want to work together in a flexible, secure and collaborative way from wherever they are. More datacenters, in more regions of the world, than all competitors combined means



Using artificial intelligence to analyze roads

Imagine you’re driving down the road. As long as the road is straight, you can see everything in front of you. But what happens when the road curves? Your brain makes assumptions based on past experiences to fill in what can’t be seen. For autonomous vehicles, the challenge is to give them the ability to make the same assumptions.

Our customer Elektronische Fahrwerksysteme GmbH (EFS) is working on that problem for a major auto manufacturer. We recently published a case study that describes how EFS uses NVIDIA GPUs in Microsoft Azure to analyze 2D images.

EFS had never applied deep learning to this kind of image processing before. Using Azure allowed them to quickly create a proof of concept environment. This allowed them to verify their algorithms and show value without having to make large upfront investments in time and capital.

“The innovative ideas we’ve implemented so far give us trust in a new deep learning architecture and in solutions that will rely on it,” EFS software developer Max Jesch said. “We proved that it’s possible to use deep learning to analyze roads. That is a really big deal. As far as we know, EFS is the first company to



Azure Databricks, industry-leading analytics platform powered by Apache Spark™

This blog post was co-authored by Ali Ghodsi, CEO, Databricks.

The confluence of cloud, data, and AI is driving unprecedented change. The ability to utilize data and turn it into breakthrough insights is foundational to innovation today. Our goal is to empower organizations to unleash the power of data and reimagine possibilities that will improve our world.

To enable this journey, we are excited to announce the general availability of Azure Databricks, a fast, easy, and collaborative Apache® Spark™-based analytics platform optimized for Azure.

Fast, easy, and collaborative

Over the past five years, Apache Spark has emerged as the open source standard for advanced analytics, machine learning, and AI on Big Data. With a massive community of over 1,000 contributors and rapid adoption by enterprises, we see Spark’s popularity continue to rise.

Azure Databricks is designed in collaboration with Databricks whose founders started the Spark research project at UC Berkeley, which later became Apache Spark. Our goal with Azure Databricks is to help customers accelerate innovation and simplify the process of building Big Data & AI solutions by combining the best of Databricks and Azure.

To meet this goal, we developed Azure Databricks with three design principles.

First, enhance



Join Microsoft at the GPU technology conference

High-performance computing, artificial intelligence, and visualization GPUs have a wide variety of uses. That’s why Microsoft has partnered with NVIDIA to bring a wide variety of NVIDIA GPUs to Azure. Join us in San Jose next week at NVIDIA’s GPU technology conference to learn how Azure customers combine the flexibility and elasticity of the cloud with the capability of NVIDIA’s GPUs.

At Booth 603, Microsoft and partners will have demos of customer use cases and experts on hand to talk about how Azure is the cloud for any GPU workload. We will have demos from our partners at Altair, PipelineFX, and Workspot. In addition, you can learn about work we’ve done in oil & gas, automotive, and artificial intelligence.

Partner and customer sessions in the conference program include:

Transforming the AEC business with cloud workstations in Azure – Jimmy Chang (Workspot) Deploying machine learning on the oilfield: from the labs to the edge – Matthieu Boujonnier (Schneider Electric), Bartosz Boguslawski (Schneider Electric), Loryne Bissuel-Beauvais (Schneider Electric) Autodesk BIM Cloud Workspace on Azure (panel) – Frank Wolbertus (TBI), Adam Jull (IMSCAD Global), Marc Sleegers (Autodesk), Allen Furmanski (Citrix Systems) Identifying new therapeutics for Parkinson’s Disease using virtual neurons on an Azure-hosted



Text Recognition for Video in Microsoft Video Indexer

In Video Indexer, we have the capability for recognizing display text in videos. This blog explains some of the techniques we used to extract the best quality data. To start, take a look at the sequence of frames below.

Source: Keynote-Delivering mission critical intelligence with SQL Server 2016

Did you manage to recognize the text in the images? It is highly reasonable that you did, without even noticing. However, using the best Optical Character Recognition (OCR) service for text extraction on these images, will yield broken words such as “icrosof”, “Mi”, “osoft” and “Micros”, simply because the text is partially hidden in each image.

There is a misconception that AI for video is simply extracting frames from a video and running computer vision algorithms on each video frame but video processing is much more than processing individual frames using an image processing algorithm – for example, with 30 frames per second, a minute-long video is 1800 frames producing a lot of data but, as we see above, not many meaningful words. There is a separate blog that covers how AI for video is different from AI for images.

While humans have cognitive abilities that allow them to complete hidden parts



New machine-assisted text classification on Content Moderator now in public preview

This blog post is co-authored by Ashish Jhanwar, Data Scientist, Microsoft

Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review.

The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review.

In contrast to the existing text moderation service that flags profanity terms, the text classification feature helps detect potentially undesired content that may be deemed as inappropriate depending on context. In addition, to convey the likelihood of each category it may recommend a human review of the content.

The text classification feature is in preview and supports the English language.

How to use

Content Moderator consists of a set of REST APIs. The text moderation API adds an additional request parameter in the form of classify=True. If you specify the parameter as true, and the auto-detected language of your input text is English, the API will output the additional classification insights as shown in the following sections.

If you specify the language as English for non-English text,



Azure cloud data and AI services training roundup

Looking to transform your business by improving your on-premises environments? Accelerating your move to the cloud, and gaining transformative insights from your data? Here’s your opportunity to learn from the experts and ask the questions that help your organization move forward.

Join us for one or all of these training sessions to take a deep dive into a variety of topics. Including products like Azure Cosmos DB, along with Microsoft innovations in artificial intelligence, advanced analytics, and big data. 

Azure Cosmos DB

Engineering experts are leading a seven-part training series on Azure Cosmos DB, complete with interactive Q&As. In addition to a high-level technical deep dive, this series covers a wide array of topics, including:

Graph API Table API Building Mongo DB apps

By the end of this series, you’ll be able to build serverless applications and conduct real-time analytics using Azure Cosmos DB, Azure Functions, and Spark. Register to attend the whole Azure Cosmos DB series, or register for the sessions that interest you.

Artificial Intelligence (AI)

Learn to create the next generation of applications spanning an intelligent cloud as well as an intelligent edge powered by AI. Microsoft offers a comprehensive set of flexible AI services for any



Microsoft and Esri launch Geospatial AI on Azure

Integrating geography and location information with AI brings a powerful new dimension to understanding the world around us. This has a wide range of applications in a variety of segments, including commercial, governmental, academic or not-for-profit. Geospatial AI provides robust tools for gathering, managing, analyzing and predicting from geographic and location-based data, and powerful visualization that can enable unique insights into the significance of such data.

Available today, Microsoft and Esri will be offering the GeoAI Data Science Virtual Machine (DSVM) as part of our Data Science Virtual Machine/Deep Learning Virtual Machine family of products on Azure. This is a result of a collaboration between the two companies and will bring AI, cloud technology and infrastructure, geospatial analytics and visualization together to help create more powerful and intelligent applications.

At the heart of the GeoAI Virtual Machine is ArcGIS Pro, Esri’s next-gen 64-bit desktop geographic information system (GIS) that provides professional 2D and 3D mapping in an intuitive user interface. ArcGIS Pro is a big step forward in advancing visualization, analytics, image processing, data management and integration.

ArcGIS Pro is installed in a Data Science Virtual Machine (DSVM) image from Microsoft. The DSVM is a popular experimentation and modeling



LUIS.AI: Automated Machine Learning for Custom Language Understanding

This blog post was co-authored by Riham Mansour, Principal Program Manager, Fuse Labs.

Conversational systems are rapidly becoming a key component of solutions such as virtual assistants, customer care, and the Internet of Things. When we talk about conversational systems, we refer to a computer’s ability to understand the human voice and take action based on understanding what the user meant. What’s more, these systems won’t be relying on voice and text alone. They’ll be using sight, sound, and feeling to process and understand these interactions, further blurring the lines between the digital sphere and the reality in which we are living. Chatbots are one common example of conversational systems.

Chatbots are a very trendy example of conversational systems that can maintain a conversation with a user in natural language, understand the user’s intent and send responses based on the organization’s business rules and data. These chatbots use Artificial Intelligence to process language, enabling them to understand human speech. They can decipher verbal or written questions and provide responses with appropriate information or direction. Many customers first experienced chatbots through dialogue boxes on company websites. Chatbots also interact verbally with consumers, such as Cortana, Siri and Amazon’s Alexa. Chatbots are